Image signal‐to‐noise ratio estimation using the autoregressive model

KS Sim, NS Kamel - Scanning: The Journal of Scanning …, 2004 - Wiley Online Library
Scanning: The Journal of Scanning Microscopies, 2004Wiley Online Library
In the last two decades, a variety of techniques for signal‐to‐noise ratio (SNR) estimation in
scanning electron microscope (SEM) images have been proposed. However, these
techniques can be divided into two groups: first, SNR estimators of good accuracy, but
based on impractical assumptions; second, estimators based on realistic assumptions but of
poor accuracy. In this paper we propose the implementation of autoregressive (AR)‐model
interpolation as a solution to the problem. Unlike others, the proposed technique is based on …
Abstract
In the last two decades, a variety of techniques for signal‐to‐noise ratio (SNR) estimation in scanning electron microscope (SEM) images have been proposed. However, these techniques can be divided into two groups: first, SNR estimators of good accuracy, but based on impractical assumptions; second, estimators based on realistic assumptions but of poor accuracy. In this paper we propose the implementation of autoregressive (AR)‐model interpolation as a solution to the problem. Unlike others, the proposed technique is based on a single SEM image and offers the required accuracy and robustness in estimating SNR values.
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